TY - GEN
T1 - Generating Learning Sequences Using Contextual Bandit Algorithms
AU - Nguyen, Le Minh Duc
AU - Lin, Fuhua
AU - Chang, Maiga
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Personalized learning paths have become a promising instructional strategy in online learning, as they can cater to individual learners’ needs and preferences. However, creating effective personalized learning paths is a complex task due to the high degree of variability in learners’ characteristics, behaviors, and learning contexts. Existing recommendation methods do not adequately address this challenge, as they do not work effectively in dynamic environments. This paper tries to address this gap by proposing a personalized learning path recommendation system using a contextual multi-armed bandit approach to offer a student an optimal learning sequence and provide the student with a modified sequence when re-planning is required.
AB - Personalized learning paths have become a promising instructional strategy in online learning, as they can cater to individual learners’ needs and preferences. However, creating effective personalized learning paths is a complex task due to the high degree of variability in learners’ characteristics, behaviors, and learning contexts. Existing recommendation methods do not adequately address this challenge, as they do not work effectively in dynamic environments. This paper tries to address this gap by proposing a personalized learning path recommendation system using a contextual multi-armed bandit approach to offer a student an optimal learning sequence and provide the student with a modified sequence when re-planning is required.
KW - Multi-Armed bandit (MAB) algorithms
KW - adaptive learning
KW - exploration and exploitation
KW - knowledge components (KC)
KW - personalized learning
UR - http://www.scopus.com/inward/record.url?scp=85195856339&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-63028-6_26
DO - 10.1007/978-3-031-63028-6_26
M3 - Published Conference contribution
AN - SCOPUS:85195856339
SN - 9783031630279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 320
EP - 329
BT - Generative Intelligence and Intelligent Tutoring Systems - 20th International Conference, ITS 2024, Proceedings
A2 - Sifaleras, Angelo
A2 - Lin, Fuhua
T2 - 20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024
Y2 - 10 June 2024 through 13 June 2024
ER -